论文标题
大数据分析架构设计
Big data analytics architecture design
论文作者
论文摘要
客观的。我们提出了一种方法，以推理目标，障碍，并选择合适的大数据解决方案架构，以满足在存在决策结果不确定性的情况下利益相关者的质量目标偏好和约束。该方法将突出可能阻碍目标的情况。他们将被评估并决心以产生建筑解决方案的完整要求。方法。该方法采用以目标为导向的建模来确定障碍，从而导致质量目标失败及其相应的分辨率策略。它结合了模糊逻辑，以探索解决方案架构中的不确定性，并为制造系统的大数据启用过程找到一组最佳的架构决策。结果。该方法为制造系统中的大数据分析平台采用了两项创新。首先，用于探索目标和数据分析平台的目标和障碍的系统为导向的建模，并在需求级别探索数据分析平台，其次，对不确定性结合利益相关者偏好的建筑决策进行了系统分析。该方法的功效通过将超连接的制造协作系统重新设计为新的大数据架构进行了说明。关键字。大数据，大数据分析平台，制造系统，面向目标的建模，模糊逻辑
Objective. We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method. The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result. The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems. Firstly, A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and, secondly, A systematic analysis of the architectural decisions under uncertainty incorporating the preferences of stakeholders. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture. Keywords. big data, big data analytics platforms, manufacturing systems, goal-oriented modeling, fuzzy logic
